Deep Learning in Smart Health: Methodologies, Applications, Challenges

Şimşek, Murat; Obinikpo, Alex Adim; Kantarcı, Burak

doi:10.1007/978-3-030-27844-1_3

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…Moreover, particular AI applications, such as chatbots, have helped to improve the efficiency and privacy of the process of receiving and addressing patients' inquiries (Nadarzynski et al, 2019;Vimalkumar et al, 2021). AI enjoys a wide range of practical features that make such systems much smarter and more efficient in comparison with human intelligence, especially in relation to visuospatial processing speed, pattern recognition, and prediction of disease, thereby enabling better diagnoses (Kummitha, 2020;Simsek et al, 2020;Wahl et al, 2018). Therefore, AI can perform critical healthcare tasks as well as or better than humans, such as the diagnosis of diseases using the AI-based COVID-19 problem-solving task (Davenport & Kalakota, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…AI is superior in some respects to human intelligence, such as in visuospatial processing speed and pattern recognition, but it lags behind in terms of reasoning, new skill learning, and creativity (Wahl et al, 2018). Prediction of disease and improving an individual's healthcare can be made more efficient by integrating computing systems with AI methodologies (Simsek et al, 2020). AI-based healthcare technologies can imitate human intelligence by classifying and predicting patient diseases using specific predictive and analytical approaches (Raza, 2020).…”

Section: Usage Of Ai-based Healthcare Technologiesmentioning

confidence: 99%

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Artificial intelligence-based public healthcare systems: G2G knowledge-based exchange to enhance the decision-making process

Nasseef

Baabdullah

Alalwan

et al. 2022

Government Information Quarterly

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Section: Discussionmentioning

confidence: 99%

Section: Usage Of Ai-based Healthcare Technologiesmentioning

confidence: 99%

Artificial intelligence-based public healthcare systems: G2G knowledge-based exchange to enhance the decision-making process

Nasseef

Baabdullah

Alalwan

et al. 2022

Government Information Quarterly

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“…To determine pair wise similarity, [16] used lexical and perceptual similarity retrieval as well as weighted network creation. The quickest route is constructed graphs return photos that are similar to the search picture.…”

Section: Literature Reviewmentioning

confidence: 99%

The Detection and Classification from the Multimodal Images using Artificial Intelligence for Lung Diseases

Hussein Abdulqader Hussein

2024

jes

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Lung cancer is a disease in which healthy cells in the body gradually convert into tumour cells, resulting in a variety of medical issues. A standard dataset exists for lung cancer. With the rising incidence of lung cancer and the exponential growth of CT pictures, having a quick and effective way to evaluate CT scans can help physicians or surgeons develop an early treatment plan. In this research, two approaches are investigated for lung cancer prediction. One approach is based on training machine learning model on the features extracted from image processing techniques. And the other approach involves Artificial Intelligence models for lung cancer detection. Computed Tomography (CT) pictures are useful for determining the stage of lung cancer in a patient. As a result, CT images of the lung region are explored in this study by constructing a content-based image retrieval system using various machine learning and Artificial Intelligence techniques. For medical photos, texture analysis is critical. As a result, algorithms such as the K-Means clustering method and morphological operations such as erosion, dilation, and so on are used

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“…Multilayer neural networks in deep learning are capable of taking into account the complex nonlinear mathematical relationships among measures that challenged previous outcome modeling strategies [ 7 ]. In the biomedical field, deep learning methods increasingly perform a vital role in inference as part of smart healthcare systems [ 8 ]. Yang et al proposed a convolutional neural network and predicted an expanded disability status scale (EDSS) to measure the severity of multiple sclerosis [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb Amputation

et al. 2022

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We created an overall assessment metric using a deep learning autoencoder to directly compare clinical outcomes in a comparison of lower limb amputees using two different prosthetic devices—a mechanical knee and a microprocessor-controlled knee. Eight clinical outcomes were distilled into a single metric using a seven-layer deep autoencoder, with the developed metric compared to similar results from principal component analysis (PCA). The proposed methods were used on data collected from ten participants with a dysvascular transfemoral amputation recruited for a prosthetics research study. This single summary metric permitted a cross-validated reconstruction of all eight scores, accounting for 83.29% of the variance. The derived score is also linked to the overall functional ability in this limited trial population, as improvements in each base clinical score led to increases in this developed metric. There was a highly significant increase in this autoencoder-based metric when the subjects used the microprocessor-controlled knee (p < 0.001, repeated measures ANOVA). A traditional PCA metric led to a similar interpretation but captured only 67.3% of the variance. The autoencoder composite score represents a single-valued, succinct summary that can be useful for the holistic assessment of highly variable, individual scores in limited clinical datasets.

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Deep Learning in Smart Health: Methodologies, Applications, Challenges

Cited by 6 publications

References 34 publications

Artificial intelligence-based public healthcare systems: G2G knowledge-based exchange to enhance the decision-making process

Artificial intelligence-based public healthcare systems: G2G knowledge-based exchange to enhance the decision-making process

The Detection and Classification from the Multimodal Images using Artificial Intelligence for Lung Diseases

Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb Amputation

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